The explosive growth of wireless services in recent years illustrates the huge and growing demand for the spectrum. Unfortunately, wireless spectrum is a very scarce resource. A promising solution to solve the predicament is the cognitive radio technology.
Cognitive radio technology allows spectrum reuse in various dimensions including space, frequency and time, so as to obliterate the spectrum and bandwidth limitations. It is expected that cognitive radio systems can sense its environment and then alter its transmission power, occupied frequency, modulation and other parameters to dynamically reuse the available spectrum.
In an orthogonal frequency-division multiplexing (OFDM) based cognitive radio system, the transmitter may allocate the data transmission rate to the subcarriers adaptively according to the different detected or approximated channel conditions of the subcarriers. It may simply avoid transmission in the narrow/partial band interference jammed subcarriers to alleviate the destructive effect of the interference.
Unfortunately, spectrum sensing is a challenging research problem and the current sensing techniques cannot guarantee accurate detection of the interference in many practical situations. Coding is essential to achieve an acceptable error rate performance. An optimal decoding in the maximum likelihood sense can be performed given the noise and interference distribution. However, the exact knowledge of the interference distribution is normally hard to obtain in reality.
For example, when the interference is bursty, the transmitter may not be kept updated fast enough and, thus, may not know the existence of the interference in a microscopic time scale (e.g., packet level). Hence, the receiver is required to decode the transmitted packets with acceptable error rate in the presence of unknown interference.
In many real-world communication systems, the channel noise present at the decoder is impulsive noise as well as the background Gaussian noise. For example, in situations such as wireless communications, power line communications and digital subscriber line loops, the non-Gaussian distribution of channel noise results from the presence of impulsive noise, such as narrowband interference for example, in addition to the background Gaussian noise. In such situations, the conventional Euclidean distance based decoder may suffer from the problem of severe metric mismatch.
Various models have been developed to approximate the characteristics of such non-Gaussian noise. However, due to the time-varying nature of the impulsive noise, it is difficult to estimate its distribution accurately. As a result of metric mismatch in the decoder, this can lead to seriously degraded decoder performance. Moreover, the difficulty in selecting an appropriate noise model presents an additional problem. Type-based detection techniques have been proposed for unknown impulsive noise channels that make no assumptions on the noise Probability Density function (PDF). However, these schemes require training sequences, which introduce additional overhead to the transmissions.
One widely used suboptimal receiver that does not require the knowledge of the noise PDF and additional training sequence, is the concatenation of a nonlinear filter with a conventional Gaussian receiver. Commonly used nonlinear filters include the limiter and hole puncher (blanker). It has been shown that the application of the nonlinear filter provides a performance improvement over the conventional Gaussian receiver alone in impulsive noise environment. The hole puncher can be interpreted as an erasure marker, where received signals that fall into particular regions in the signal space are erased. The rationale is that if a received signal is determined to be unreliable, it is better to ignore it rather than use it in further detection process.
Channel coding has also been applied to further mitigate the adverse effect of impulsive noise. The idealized erasure marker, where the impulse positions are exactly known, has been considered in combination with the hard decision Viterbi decoder and the turbo decoder. In one implementation, the received code symbols are first marked by the decision region based erasure marker and then sent to the Euclidean metric based Viterbi decoder to decode. However, the separation of the erasure marker and the decoder leads to less accurate detection of the impulse-corrupted symbols because the code structure is not exploited.
Another fundamental problem in modern wireless communication systems is caused by interference from other communication systems sharing the same frequency band (e.g., narrowband interference). For example, IEEE 802.11g Wireless Local Area Network (WLAN) systems operate in the same frequency band as Bluetooth systems, which are narrowband frequency-hopping systems. For a typical 200 μs long WLAN packet, the probability of collision with a Bluetooth packet is more than 20%. Since the frequency-hopping rate of Bluetooth is relatively high, it is not easy for the WLAN to sense the presence of the Bluetooth transmissions. Moreover, the Bluetooth traffic is bursty since it only transmits data during the first 366 μs of each 625 μs time slot. It is likely that the Bluetooth data packet may only collide with the data part of the WLAN packet without interfering with the pilot symbols. In such situations, the pilot aided interference detection is unable to detect the presence of the interference.
When narrowband interference exists, the noise variance is not a constant for all the subcarriers and dropping the noise variance in the bit metric results in metric mismatch. When the interference power is high, the mismatch problem can be serious. Knowing the interference jammed subcarriers and the power of the interference, the optimal decoder, in the maximum likelihood sense, weights each symbol differently depending on whether the symbol is hit by background Gaussian noise or interference.
Without knowing the impulsive noise probability density function, one promising solution is to identify the symbols that are likely to be corrupted by large amplitude noise and ignore (erase) them in decoding. A reasonable alternative decoding methodology is to simply ignore or erase these jammed symbols in decoding and assume a constant noise variance for the unerased symbols. Since the interference power is normally much larger than the background Gaussian noise power, the performance loss would be small by treating the jammed symbols as erasures. Such an erasure decoding approach avoids the requirement of knowing the interference power. The decoding accuracy then highly depends on the estimation accuracy of the interference positions.
In such examples, interference jammed signals are erased (e.g., ignored) in the decoding process, thus partially avoiding the adverse effect of the interference. Although they do not require the knowledge of the interference power, the decoding schemes need to know the presence of the interference. In one example, the null symbols (zero symbols) are sent as pilots for the receiver to detect the interference. The null symbols, however, increase system overhead and reduce the system throughput. A simple method to detect the interference is based on the magnitude variation of the consecutive received symbols in time or frequency domain. The effectiveness of this method is reduced for high order modulation schemes or when the fading varies significantly. For example, if the interference is estimated based on the rough estimate of the transmitted data symbols, the accuracy of this method is limited by the erroneous estimates of the transmitted data.
As a result, the exact knowledge of the interference distribution is normally hard to obtain in reality. Therefore, it is practically important to design robust decoding schemes that do not require the accurate knowledge of the noise distribution. It is further desired to exploit the code structure in interference detection, to effectively detect almost all the interference jammed symbols while being able to achieve a performance close to that of the optimal maximum likelihood decoder with the full knowledge of the interference distribution. Furthermore, as different decoding problems require different approaches dictated by power usage and availability restrictions, hardware costs and space limitations, and further design considerations, it is further desired to provide alternative decoding schemes that can provide design flexibility and hardware tradeoffs (e.g., computation complexity, decoding delay and memory requirement) while maintaining acceptable decoder error rate performance. Accordingly, a further consideration is to achieve a low error rate while minimizing the decoder complexity.
The above-described deficiencies are merely intended to provide an overview of some of the problems encountered in non-Gaussian decoder design, and are not intended to be exhaustive. Other problems with the state of the art may become further apparent upon review of the description of the various non-limiting embodiments of the disclosed subject matter that follows.